{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T09:12:32Z","timestamp":1767690752552,"version":"3.48.0"},"reference-count":25,"publisher":"Institution of Engineering and Technology (IET)","issue":"1","license":[{"start":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T00:00:00Z","timestamp":1767657600000},"content-version":"vor","delay-in-days":5,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62471181"],"award-info":[{"award-number":["62471181"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["IET Image Processing"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>To address the challenges of feature loss, inaccurate localization, and false or missed detections in small\u2010object detection of loosened and spaced nuts in power transmission lines, this study proposes an enhanced detection model, you only look once\u2010EPDS (YOLO\u2010EPDS), built upon an improved YOLOv9 framework. A RepNCSPELAN4_EMA module is integrated into the backbone network to incorporate a multi\u2010scale attention mechanism, enhancing the extraction of subtle nut texture features via cross\u2010space interactions and parallel multi\u2010branch feature recalibration. SPD\u2010Conv modules replace conventional downsampling layers in the backbone, effectively preserving spatial details in feature maps. Additionally, a RepNCSPELAN4_DCNv4 module employs dynamic deformable convolutions (DCNv4) to improve adaptability to geometrically deformed objects. The shape\u2010IoU loss function is utilized to optimize bounding box regression for small objects. Experimental results indicate that the proposed model achieves a mAP@50 of 79.7% on a self\u2010constructed transmission line nut dataset, outperforming the baseline by 4.3%. These enhancements synergistically increase confidence scores while reducing false\u2010positive and false\u2010negative rates, demonstrating superior capability in extracting defective features of loosened nuts and substantially improving the reliability of transmission line inspection.<\/jats:p>","DOI":"10.1049\/ipr2.70279","type":"journal-article","created":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T09:07:33Z","timestamp":1767690453000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["YOLO\u2010EPDS: A Small Object Detection Algorithm for Power Transmission Line Nut Spacing Looseness"],"prefix":"10.1049","volume":"20","author":[{"given":"Guilan","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Computer North China Electric Power University  Baoding Hebei China"},{"name":"Hebei Key Laboratory of Knowledge Computing For Energy and Power  Baoding Hebei China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5119-6871","authenticated-orcid":false,"given":"Zenglei","family":"Hao","sequence":"additional","affiliation":[{"name":"Department of Computer North China Electric Power University  Baoding Hebei China"},{"name":"Hebei Key Laboratory of Knowledge Computing For Energy and Power  Baoding Hebei China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4061-6822","authenticated-orcid":false,"given":"Wangbin","family":"Cao","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering North China Electric Power University  Baoding Hebei China"},{"name":"Hebei Key Laboratory of Power Internet of Things Technology Baoding Hebei China"}]},{"given":"Huawei","family":"Mei","sequence":"additional","affiliation":[{"name":"Department of Computer North China Electric Power University  Baoding Hebei China"},{"name":"Engineering Research Center of Intelligent Computing For Complex Energy Systems Ministry of Education  Baoding Hebei China"}]}],"member":"265","published-online":{"date-parts":[[2026,1,6]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/s43588\u2010022\u201000341\u2010x"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2025.125507"},{"key":"e_1_2_9_4_1","doi-asserted-by":"crossref","unstructured":"F.Neha D.Bhati D. 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